efficientnet_v2_m¶
- lucid.models.efficientnet_v2_m(num_classes: int = 1000, **kwargs) EfficientNet_V2¶
The efficientnet_v2_m function instantiates a medium-sized variant of the EfficientNet-v2 model, specifically designed for lightweight tasks while maintaining high performance.
Total Parameters: 55,302,108
Function Signature¶
@register_model
def efficientnet_v2_m(num_classes: int = 1000, **kwargs) -> EfficientNet_V2:
Parameters¶
num_classes (int, optional): The number of output classes for the model. Default is 1000.
kwargs (dict, optional): Additional keyword arguments for configuring the EfficientNet_V2 model. These parameters are passed directly to the underlying constructor of EfficientNet_V2.
Returns¶
EfficientNet_V2: An instance of the EfficientNet_V2 model pre-configured for the small variant.
Usage¶
The efficientnet_v2_m function simplifies the creation of an EfficientNet-v2 model for lightweight tasks such as image classification on smaller datasets. The configuration ensures an optimized balance between efficiency and accuracy.
Examples¶
Creating a EfficientNet-v2-M model:
import lucid
import lucid.models as models
# Instantiate the medium-sized variant of EfficientNet_V2
model = models.efficientnet_v2_m(num_classes=100, dropout=0.2)
# Create a sample input tensor
input_tensor = lucid.random.randn(1, 3, 224, 224)
# Perform a forward pass
output = model(input_tensor)
print(output)